When pundits like David Brooks get sucked into the factoid-warp of Hanna Rosin (The End of Men) and Liza Mundy (The Richer Sex), they are always floored by the idea that young women earn more than young men. To them this represents the future. And woe to any woman trying to convince a jury she’s being discriminated against while these books are in the headlines. Brooks spelled it out real simple: “Women in their 20s outearn men in their 20s.”

That’s easily shown to be wrong (still holding my breath for the correction). But the more detailed factoid, the one you get in the long-soundbite version of the end-of-history, is that “median full-time wages for single childless women ages 22-30 exceeds those of single childless men in the same age group,” as reported in USA Today, for example. That was calculated by Reach Advisors using the American Community Survey.*

Making broad conclusions based on weird data slices is bad practice. And this is a great case study in why.

Who are those full-time working, not-married and childfree 20-somethings in metro areas? I ran that filter over the 2010 ACS data available from IPUMS, and this jumped out:

OK, so for whatever reason, notice that this group includes a disproportionate share of White women and Latino men. That turns out to be pivotal, since these particular Latino men have very low earnings. Check the earnings by race/ethnicity and gender:

So that’s it. The overall $1,000 advantage for women (seen in the bars on the far right) is the result of these particular Latino men’s low earnings. The high earnings of these White women are important, of course, they’re just not higher than White men’s. If you just look at Whites or Blacks there is no advantage for women.

I am all for getting into the problem of Latino men’s (and women’s) low average earnings. But that’s not where this story has been going. More than anything this is just shoddy statistical cherry-picking.

Hey media mega-conglomerates: give that meme a rest!

* Reach Advisors also limited the analysis to metro areas, so I did that as well. I don’t get as big an advantage for “women” as that reported in that 2010 USA Today article, which said it was based on 2008 data (they got an 8% gap, I get 3%). I don’t care to figure out exactly the source of the differences (and Reach hasn’t published their code).

First,is the difference between the BLS figures and the Reach Advisors analysis likely to be as simple as one word: “childless”? I can’t see any other reason for a noticeable difference between the two data sets, can you?

Secondly, this bit about the ACS figures “this group includes a disproportionate share of White women and Latino men.”

Do you know exactly what the ethnic breakdown of this data set *should* be? Are you sure it is not an accurate representation of the population? I can think of various reasons why there might be gender differences in population groups (imprisonment, postgraduate education, different mortality rates, disability status etc etc etc.)

Now,to get really geeky, assuming you’re right that the ethnic breakdown is disproportionate, have you been able to calculate whether this disproportionality entirely accounts for the $1000 difference in the totals? If there were a perfectly representative sample, would women still come out slightly ahead in earnings?

(I appreciate all this stuff is only as valid as the accuracy of available population data, which is a whole can of worms, but your best hunch would still be of interest!)

Thanks. I don’t know which BLS numbers you are comparing this to, so I can’t answer that. But “childless” makes a big difference since single mothers in their twenties are quite poor on average.

Second, by “disproportionate” I didn’t mean the data are wrong, just that there are more white women than white men and more Latino men than Latina women. It’s a weird selection group so I didn’t have a prior expectation about what the population would look like.

Finally, you can tell the race/ethnic composition makes the difference because Latinos are the only group where men earn less – without them there wouldn’t be a female advantage (I’m not showing Asians and American Indians here, but they aren’t numerous enough to swing this.)

(Geekily speaking, that doesn’t mean something else doesn’t *also* “account” for the gender gap, such as educational attainment. There are ways to consider multiple factors at once, the simplest of which is multiple regression. Which I haven’t done here.)

the BLS figures I meant were the one in your previous blog, the table showing median income for different age groups which put the women’s medians at 88% & 91% of men’s for the 20-24 and 25-34 age groups. As far as I can see, that doesn’t exclude childless women so I was suggesting that might explain the difference.

I appreciate the point you were making about the different ethnic groups accounting for the whole difference. The point I was trying to get across is that if the ethnic breakdown is reasonably accurate, it doesn’t make Rosin (or Chung, who she got her figures from) wrong to say that women outearn men in this specific category in this sample, it just gives an interesting explanation as to why.

On the other hand, if we knew that the sample had a disproportionate number of Latino men compared to the actual population, it would cast a fairly big cloud over the credibility of the figures.

The conclusion I’d come to, I think, is that for young, childless people in full time work, the difference between male and female earnings are so negligible as to be meaningless. Whether one or the other is a fraction higher or a fraction lower seems fairly unimportant to me.

Does that seem fair?

I’d add, the exclusion of childless women from this discussion is a really good example of the single biggest weakness in Rosin’s case. I’m in the UK but there are similar trends happening here, and it looks to me like, despite her taste for hyperbole, the broad thrust of Rosin’s argument is broadly true – right up to the point that people have children, at which point virtually all the statistics start lurching spectacularly back where they came from. Yes, there are far more men taking an equal or lead role in childcare / homekeeping than there used to be (I’m one of them) but there are still far, far more women and we’re a long way away from reinventing the old patriarchal nuclear family just yet.

Do you care to elaborate? It seems that the meaning of that word is pivotal to your argument.

Why are Latinos an outlier?

(Maybe they are not an “outlier”? That’s an actual term that people use in statistics that they actually have to defend. Perhaps the word “weird” allows you to construct a narrative, but not actually defend things in a statistical manner?)

It’s cool. Whatever. But if you are going to play like this and simply attack other stats without positing alternative hypotheses (why are latinos different than whites? You have to answer this in your criticism…. Any professor would write this in a critique) I’m not sure why I should listen to you,

Why should I listen to you? Because anyone with access to more granular data on the “Latino community” could rip your “getting into” final comment to shreds with actual numbers.

More likely.. the whole concept of “outliers” is simply a problem of coding. Big data can help… but it needs to be unbiased big data.

Funny, the only group where men earn more than women is amongst “blacks”. Latino women actually earn more, white men and women earn the same, and overall women earn more than men. The gap is BUSTED people, let it go.

It shows that Latinas have babies and have them earlier. Which, relatively, increases the percentage of women from the other major racial groups, black and white. Although, suggests the others, as they remain proportionately the same, also have children earlier. Although, its hard to say for sure from the stats, as they are a small percentage.

The Latino stats are odd but, perhaps, reflect “machismo” in that culture? But, given the wage levels are low for both men and women this, perhaps, reflects a greater number in low paid, low skill “blue collar” jobs. So, for example, the men are working in construction and the women in cleaning. And the former earns more.

After parenthood there is a “fatherhood credit” and “motherhood deficit”, which distorts the stats after that point.